Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations515738
Missing cells6536
Missing cells (%)0.1%
Duplicate rows336
Duplicate rows (%)0.1%
Total size in memory70.8 MiB
Average record size in memory144.0 B

Variable types

Text7
Numeric9
DateTime1
Categorical1

Alerts

Dataset has 336 (0.1%) duplicate rowsDuplicates
additional_number_of_scoring is highly overall correlated with total_number_of_reviewsHigh correlation
reviewer_score is highly overall correlated with sampleHigh correlation
sample is highly overall correlated with reviewer_scoreHigh correlation
total_number_of_reviews is highly overall correlated with additional_number_of_scoringHigh correlation
review_total_negative_word_counts has 127890 (24.8%) zeros Zeros
review_total_positive_word_counts has 35946 (7.0%) zeros Zeros
reviewer_score has 128935 (25.0%) zeros Zeros

Reproduction

Analysis started2025-05-07 11:01:45.553309
Analysis finished2025-05-07 11:02:23.688213
Duration38.13 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct1493
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:23.912101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length96
Median length78
Mean length59.879357
Min length34

Characters and Unicode

Total characters30882060
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVia Senigallia 6 20161 Milan Italy
2nd rowArlandaweg 10 Westpoort 1043 EW Amsterdam Netherlands
3rd rowMallorca 251 Eixample 08008 Barcelona Spain
4th rowPiazza Della Repubblica 17 Central Station 20124 Milan Italy
5th rowSingel 303 309 Amsterdam City Center 1012 WJ Amsterdam Netherlands
ValueCountFrequency (%)
london 283657
 
5.7%
kingdom 262692
 
5.2%
united 262301
 
5.2%
westminster 95105
 
1.9%
borough 90619
 
1.8%
amsterdam 82634
 
1.7%
city 62594
 
1.3%
barcelona 61231
 
1.2%
street 60434
 
1.2%
spain 60149
 
1.2%
Other values (2525) 3684801
73.6%
2025-05-07T14:02:24.232442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4490884
 
14.5%
n 2381541
 
7.7%
e 2224973
 
7.2%
a 1876434
 
6.1%
o 1633811
 
5.3%
t 1600746
 
5.2%
r 1481491
 
4.8%
i 1459499
 
4.7%
d 1358260
 
4.4%
s 939735
 
3.0%
Other values (53) 11434686
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30882060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4490884
 
14.5%
n 2381541
 
7.7%
e 2224973
 
7.2%
a 1876434
 
6.1%
o 1633811
 
5.3%
t 1600746
 
5.2%
r 1481491
 
4.8%
i 1459499
 
4.7%
d 1358260
 
4.4%
s 939735
 
3.0%
Other values (53) 11434686
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30882060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4490884
 
14.5%
n 2381541
 
7.7%
e 2224973
 
7.2%
a 1876434
 
6.1%
o 1633811
 
5.3%
t 1600746
 
5.2%
r 1481491
 
4.8%
i 1459499
 
4.7%
d 1358260
 
4.4%
s 939735
 
3.0%
Other values (53) 11434686
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30882060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4490884
 
14.5%
n 2381541
 
7.7%
e 2224973
 
7.2%
a 1876434
 
6.1%
o 1633811
 
5.3%
t 1600746
 
5.2%
r 1481491
 
4.8%
i 1459499
 
4.7%
d 1358260
 
4.4%
s 939735
 
3.0%
Other values (53) 11434686
37.0%

additional_number_of_scoring
Real number (ℝ)

High correlation 

Distinct480
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.08184
Minimum1
Maximum2682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:24.326217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile57
Q1169
median341
Q3660
95-th percentile1444
Maximum2682
Range2681
Interquartile range (IQR)491

Descriptive statistics

Standard deviation500.53847
Coefficient of variation (CV)1.0049322
Kurtosis5.751927
Mean498.08184
Median Absolute Deviation (MAD)201
Skewness2.2077514
Sum2.5687973 × 108
Variance250538.76
MonotonicityNot monotonic
2025-05-07T14:02:24.415961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2682 4789
 
0.9%
2288 4256
 
0.8%
2623 4169
 
0.8%
1831 3578
 
0.7%
1936 3212
 
0.6%
256 3079
 
0.6%
1274 2958
 
0.6%
832 2934
 
0.6%
211 2858
 
0.6%
404 2836
 
0.5%
Other values (470) 481069
93.3%
ValueCountFrequency (%)
1 13
 
< 0.1%
4 12
 
< 0.1%
5 39
 
< 0.1%
6 118
< 0.1%
7 56
 
< 0.1%
8 57
 
< 0.1%
9 89
< 0.1%
10 195
< 0.1%
11 151
< 0.1%
12 67
 
< 0.1%
ValueCountFrequency (%)
2682 4789
0.9%
2623 4169
0.8%
2288 4256
0.8%
1936 3212
0.6%
1831 3578
0.7%
1485 2628
0.5%
1471 2155
0.4%
1444 2565
0.5%
1427 2227
0.4%
1322 2223
0.4%
Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
Minimum2015-08-04 00:00:00
Maximum2017-08-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-07T14:02:24.499135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:24.870621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

average_score
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3974869
Minimum5.2
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:24.961485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile7.4
Q18.1
median8.4
Q38.8
95-th percentile9.2
Maximum9.8
Range4.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.54804817
Coefficient of variation (CV)0.065263355
Kurtosis0.4223857
Mean8.3974869
Median Absolute Deviation (MAD)0.3
Skewness-0.54524262
Sum4330903.1
Variance0.3003568
MonotonicityNot monotonic
2025-05-07T14:02:25.048626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8.4 41222
 
8.0%
8.1 38122
 
7.4%
8.5 38066
 
7.4%
8.7 37798
 
7.3%
8.6 36945
 
7.2%
8.2 34847
 
6.8%
8.3 32880
 
6.4%
8.8 30836
 
6.0%
8.9 28520
 
5.5%
8 22342
 
4.3%
Other values (24) 174160
33.8%
ValueCountFrequency (%)
5.2 65
 
< 0.1%
6.4 1163
 
0.2%
6.6 400
 
0.1%
6.7 965
 
0.2%
6.8 1335
 
0.3%
6.9 1737
 
0.3%
7 3899
0.8%
7.1 6780
1.3%
7.2 684
 
0.1%
7.3 3997
0.8%
ValueCountFrequency (%)
9.8 28
 
< 0.1%
9.6 915
 
0.2%
9.5 1207
 
0.2%
9.4 9339
 
1.8%
9.3 12659
2.5%
9.2 12935
2.5%
9.1 21379
4.1%
9 21051
4.1%
8.9 28520
5.5%
8.8 30836
6.0%
Distinct1492
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:25.223201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length60
Median length43
Mean length25.30696
Min length2

Characters and Unicode

Total characters13051761
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel Da Vinci
2nd rowUrban Lodge Hotel
3rd rowAlexandra Barcelona A DoubleTree by Hilton
4th rowHotel Principe Di Savoia
5th rowHotel Esther a
ValueCountFrequency (%)
hotel 234986
 
11.6%
london 137227
 
6.8%
the 58689
 
2.9%
park 43929
 
2.2%
amsterdam 39868
 
2.0%
hilton 35490
 
1.8%
by 26928
 
1.3%
plaza 23105
 
1.1%
paris 21792
 
1.1%
grand 18430
 
0.9%
Other values (1595) 1386601
68.4%
2025-05-07T14:02:25.506011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1513162
 
11.6%
e 1229379
 
9.4%
o 1085093
 
8.3%
n 970893
 
7.4%
a 904023
 
6.9%
t 816440
 
6.3%
l 768209
 
5.9%
r 726795
 
5.6%
i 570540
 
4.4%
s 435214
 
3.3%
Other values (53) 4032013
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13051761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1513162
 
11.6%
e 1229379
 
9.4%
o 1085093
 
8.3%
n 970893
 
7.4%
a 904023
 
6.9%
t 816440
 
6.3%
l 768209
 
5.9%
r 726795
 
5.6%
i 570540
 
4.4%
s 435214
 
3.3%
Other values (53) 4032013
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13051761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1513162
 
11.6%
e 1229379
 
9.4%
o 1085093
 
8.3%
n 970893
 
7.4%
a 904023
 
6.9%
t 816440
 
6.3%
l 768209
 
5.9%
r 726795
 
5.6%
i 570540
 
4.4%
s 435214
 
3.3%
Other values (53) 4032013
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13051761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1513162
 
11.6%
e 1229379
 
9.4%
o 1085093
 
8.3%
n 970893
 
7.4%
a 904023
 
6.9%
t 816440
 
6.3%
l 768209
 
5.9%
r 726795
 
5.6%
i 570540
 
4.4%
s 435214
 
3.3%
Other values (53) 4032013
30.9%
Distinct227
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:25.704195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length14.036272
Min length1

Characters and Unicode

Total characters7239039
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row United Kingdom
2nd row Belgium
3rd row Sweden
4th row United States of America
5th row United Kingdom
ValueCountFrequency (%)
united 290992
31.9%
kingdom 245246
26.9%
of 35851
 
3.9%
states 35511
 
3.9%
america 35437
 
3.9%
australia 21686
 
2.4%
ireland 14827
 
1.6%
arab 10235
 
1.1%
emirates 10235
 
1.1%
saudi 8951
 
1.0%
Other values (262) 203907
22.3%
2025-05-07T14:02:26.000108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1428616
19.7%
i 707670
9.8%
n 664608
9.2%
d 607285
 
8.4%
e 500817
 
6.9%
t 445571
 
6.2%
a 370806
 
5.1%
o 323726
 
4.5%
m 315246
 
4.4%
U 292269
 
4.0%
Other values (42) 1582425
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7239039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1428616
19.7%
i 707670
9.8%
n 664608
9.2%
d 607285
 
8.4%
e 500817
 
6.9%
t 445571
 
6.2%
a 370806
 
5.1%
o 323726
 
4.5%
m 315246
 
4.4%
U 292269
 
4.0%
Other values (42) 1582425
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7239039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1428616
19.7%
i 707670
9.8%
n 664608
9.2%
d 607285
 
8.4%
e 500817
 
6.9%
t 445571
 
6.2%
a 370806
 
5.1%
o 323726
 
4.5%
m 315246
 
4.4%
U 292269
 
4.0%
Other values (42) 1582425
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7239039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1428616
19.7%
i 707670
9.8%
n 664608
9.2%
d 607285
 
8.4%
e 500817
 
6.9%
t 445571
 
6.2%
a 370806
 
5.1%
o 323726
 
4.5%
m 315246
 
4.4%
U 292269
 
4.0%
Other values (42) 1582425
21.9%
Distinct330011
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:26.236355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1966
Median length1910
Mean length93.798167
Min length1

Characters and Unicode

Total characters48375279
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323546 ?
Unique (%)62.7%

Sample

1st row Would have appreciated a shop in the hotel that sold drinking water etc but not necessity Would recommend if like us you arrive late at night to bring drinks from plane airport as there s no shop nearby There is a minibar though if you want to pay those prices
2nd row No tissue paper box was present at the room
3rd row Pillows
4th rowNo Negative
5th rowNo Negative
ValueCountFrequency (%)
the 531268
 
5.8%
was 236750
 
2.6%
a 230251
 
2.5%
to 228892
 
2.5%
and 219473
 
2.4%
no 197882
 
2.1%
room 176026
 
1.9%
in 168040
 
1.8%
negative 129447
 
1.4%
not 125701
 
1.4%
Other values (55627) 6961291
75.6%
2025-05-07T14:02:26.573061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9301533
19.2%
e 4710124
 
9.7%
t 3568261
 
7.4%
o 3563252
 
7.4%
a 3077470
 
6.4%
i 2442432
 
5.0%
n 2399642
 
5.0%
r 2321682
 
4.8%
s 2085748
 
4.3%
h 1783967
 
3.7%
Other values (53) 13121168
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48375279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9301533
19.2%
e 4710124
 
9.7%
t 3568261
 
7.4%
o 3563252
 
7.4%
a 3077470
 
6.4%
i 2442432
 
5.0%
n 2399642
 
5.0%
r 2321682
 
4.8%
s 2085748
 
4.3%
h 1783967
 
3.7%
Other values (53) 13121168
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48375279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9301533
19.2%
e 4710124
 
9.7%
t 3568261
 
7.4%
o 3563252
 
7.4%
a 3077470
 
6.4%
i 2442432
 
5.0%
n 2399642
 
5.0%
r 2321682
 
4.8%
s 2085748
 
4.3%
h 1783967
 
3.7%
Other values (53) 13121168
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48375279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9301533
19.2%
e 4710124
 
9.7%
t 3568261
 
7.4%
o 3563252
 
7.4%
a 3077470
 
6.4%
i 2442432
 
5.0%
n 2399642
 
5.0%
r 2321682
 
4.8%
s 2085748
 
4.3%
h 1783967
 
3.7%
Other values (53) 13121168
27.1%

review_total_negative_word_counts
Real number (ℝ)

Zeros 

Distinct402
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.53945
Minimum0
Maximum408
Zeros127890
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:26.661906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q323
95-th percentile69
Maximum408
Range408
Interquartile range (IQR)21

Descriptive statistics

Standard deviation29.690831
Coefficient of variation (CV)1.6014947
Kurtosis31.413626
Mean18.53945
Median Absolute Deviation (MAD)9
Skewness4.407949
Sum9561499
Variance881.54543
MonotonicityNot monotonic
2025-05-07T14:02:26.753948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127890
24.8%
2 24647
 
4.8%
3 18144
 
3.5%
6 17749
 
3.4%
5 16809
 
3.3%
7 16140
 
3.1%
4 15063
 
2.9%
8 14716
 
2.9%
9 13641
 
2.6%
10 12422
 
2.4%
Other values (392) 238517
46.2%
ValueCountFrequency (%)
0 127890
24.8%
2 24647
 
4.8%
3 18144
 
3.5%
4 15063
 
2.9%
5 16809
 
3.3%
6 17749
 
3.4%
7 16140
 
3.1%
8 14716
 
2.9%
9 13641
 
2.6%
10 12422
 
2.4%
ValueCountFrequency (%)
408 1
< 0.1%
403 2
< 0.1%
402 2
< 0.1%
401 1
< 0.1%
400 1
< 0.1%
399 2
< 0.1%
398 1
< 0.1%
397 1
< 0.1%
395 1
< 0.1%
393 2
< 0.1%

total_number_of_reviews
Real number (ℝ)

High correlation 

Distinct1142
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2743.7439
Minimum43
Maximum16670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:26.849169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile435
Q11161
median2134
Q33613
95-th percentile7371
Maximum16670
Range16627
Interquartile range (IQR)2452

Descriptive statistics

Standard deviation2317.4649
Coefficient of variation (CV)0.84463598
Kurtosis6.4210843
Mean2743.7439
Median Absolute Deviation (MAD)1118
Skewness2.0861703
Sum1.415053 × 109
Variance5370643.4
MonotonicityNot monotonic
2025-05-07T14:02:26.939820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9086 4789
 
0.9%
9568 4256
 
0.8%
12158 4169
 
0.8%
7105 3578
 
0.7%
7491 3212
 
0.6%
6539 2958
 
0.6%
5945 2768
 
0.5%
6977 2628
 
0.5%
5726 2565
 
0.5%
4204 2551
 
0.5%
Other values (1132) 482264
93.5%
ValueCountFrequency (%)
43 12
 
< 0.1%
45 12
 
< 0.1%
49 40
< 0.1%
51 13
 
< 0.1%
54 13
 
< 0.1%
59 75
< 0.1%
60 23
 
< 0.1%
61 17
 
< 0.1%
64 31
< 0.1%
66 12
 
< 0.1%
ValueCountFrequency (%)
16670 1877
 
0.4%
12158 4169
0.8%
10842 1118
 
0.2%
9568 4256
0.8%
9086 4789
0.9%
8177 1809
 
0.4%
7656 1576
 
0.3%
7586 1686
 
0.3%
7491 3212
0.6%
7371 1335
 
0.3%
Distinct412601
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:27.161551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1960
Median length1841
Mean length94.621069
Min length1

Characters and Unicode

Total characters48799681
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403184 ?
Unique (%)78.2%

Sample

1st row Hotel was great clean friendly staff free breakfast every morning with good selection good wifi connection nice sized room with bath fridge in room Personally loved the fact that the hotel isn t in the city centre but is literally next to a train station that you can easily get to and from the airport city Would definitely stay again
2nd rowNo Positive
3rd row Nice welcoming and service
4th row Everything including the nice upgrade The Hotel has been revamped and what a surprise Love every second of it including in room dining which was excellent
5th row Lovely hotel v welcoming staff
ValueCountFrequency (%)
the 515247
 
6.1%
and 420617
 
5.0%
was 236743
 
2.8%
staff 194574
 
2.3%
location 192856
 
2.3%
very 192743
 
2.3%
to 187933
 
2.2%
a 164977
 
1.9%
room 140746
 
1.7%
hotel 125326
 
1.5%
Other values (51225) 6120421
72.1%
2025-05-07T14:02:27.486111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8724132
17.9%
e 4823582
 
9.9%
a 3497026
 
7.2%
o 3418374
 
7.0%
t 3414477
 
7.0%
n 2492661
 
5.1%
r 2427626
 
5.0%
i 2342431
 
4.8%
s 2116857
 
4.3%
l 2084944
 
4.3%
Other values (53) 13457571
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48799681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8724132
17.9%
e 4823582
 
9.9%
a 3497026
 
7.2%
o 3418374
 
7.0%
t 3414477
 
7.0%
n 2492661
 
5.1%
r 2427626
 
5.0%
i 2342431
 
4.8%
s 2116857
 
4.3%
l 2084944
 
4.3%
Other values (53) 13457571
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48799681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8724132
17.9%
e 4823582
 
9.9%
a 3497026
 
7.2%
o 3418374
 
7.0%
t 3414477
 
7.0%
n 2492661
 
5.1%
r 2427626
 
5.0%
i 2342431
 
4.8%
s 2116857
 
4.3%
l 2084944
 
4.3%
Other values (53) 13457571
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48799681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8724132
17.9%
e 4823582
 
9.9%
a 3497026
 
7.2%
o 3418374
 
7.0%
t 3414477
 
7.0%
n 2492661
 
5.1%
r 2427626
 
5.0%
i 2342431
 
4.8%
s 2116857
 
4.3%
l 2084944
 
4.3%
Other values (53) 13457571
27.6%

review_total_positive_word_counts
Real number (ℝ)

Zeros 

Distinct365
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.776458
Minimum0
Maximum395
Zeros35946
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:27.583774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q322
95-th percentile56
Maximum395
Range395
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.804185
Coefficient of variation (CV)1.2265765
Kurtosis32.943045
Mean17.776458
Median Absolute Deviation (MAD)7
Skewness4.1911321
Sum9167995
Variance475.42249
MonotonicityNot monotonic
2025-05-07T14:02:27.685361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35946
 
7.0%
6 26921
 
5.2%
5 26844
 
5.2%
4 24656
 
4.8%
7 24538
 
4.8%
8 23238
 
4.5%
3 22533
 
4.4%
9 21208
 
4.1%
2 20934
 
4.1%
10 19611
 
3.8%
Other values (355) 269309
52.2%
ValueCountFrequency (%)
0 35946
7.0%
2 20934
4.1%
3 22533
4.4%
4 24656
4.8%
5 26844
5.2%
6 26921
5.2%
7 24538
4.8%
8 23238
4.5%
9 21208
4.1%
10 19611
3.8%
ValueCountFrequency (%)
395 1
< 0.1%
386 1
< 0.1%
384 2
< 0.1%
383 2
< 0.1%
382 1
< 0.1%
380 1
< 0.1%
378 1
< 0.1%
377 1
< 0.1%
375 2
< 0.1%
374 1
< 0.1%
Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.166001
Minimum1
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:27.781026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile26
Maximum355
Range354
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.040228
Coefficient of variation (CV)1.54064
Kurtosis51.479447
Mean7.166001
Median Absolute Deviation (MAD)2
Skewness5.0875667
Sum3695779
Variance121.88663
MonotonicityNot monotonic
2025-05-07T14:02:27.871996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154640
30.0%
2 67077
13.0%
3 46845
 
9.1%
4 35026
 
6.8%
5 27629
 
5.4%
6 22621
 
4.4%
7 18614
 
3.6%
8 16150
 
3.1%
9 13545
 
2.6%
10 11717
 
2.3%
Other values (188) 101874
19.8%
ValueCountFrequency (%)
1 154640
30.0%
2 67077
13.0%
3 46845
 
9.1%
4 35026
 
6.8%
5 27629
 
5.4%
6 22621
 
4.4%
7 18614
 
3.6%
8 16150
 
3.1%
9 13545
 
2.6%
10 11717
 
2.3%
ValueCountFrequency (%)
355 1
 
< 0.1%
330 1
 
< 0.1%
315 4
< 0.1%
297 2
< 0.1%
281 2
< 0.1%
270 2
< 0.1%
250 3
< 0.1%
239 1
 
< 0.1%
237 1
 
< 0.1%
232 1
 
< 0.1%

tags
Text

Distinct55242
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:28.101328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length213
Median length178
Mean length102.41794
Min length11

Characters and Unicode

Total characters52820822
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29892 ?
Unique (%)5.8%

Sample

1st row[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']
2nd row[' Leisure trip ', ' Group ', ' Triple Room ', ' Stayed 1 night ']
3rd row[' Business trip ', ' Solo traveler ', ' Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
4th row[' Leisure trip ', ' Couple ', ' Ambassador Junior Suite ', ' Stayed 1 night ']
5th row[' Business trip ', ' Solo traveler ', ' Classic Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']
ValueCountFrequency (%)
4713184
41.1%
stayed 515546
 
4.5%
trip 500717
 
4.4%
room 467443
 
4.1%
leisure 417900
 
3.6%
nights 321909
 
2.8%
a 309360
 
2.7%
from 307963
 
2.7%
mobile 307693
 
2.7%
device 307640
 
2.7%
Other values (644) 3306475
28.8%
2025-05-07T14:02:28.428409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10960092
20.7%
' 4713184
 
8.9%
e 4064833
 
7.7%
i 3223273
 
6.1%
o 2787265
 
5.3%
t 2606633
 
4.9%
r 2080930
 
3.9%
, 1840854
 
3.5%
u 1792540
 
3.4%
m 1539332
 
2.9%
Other values (57) 17211886
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52820822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10960092
20.7%
' 4713184
 
8.9%
e 4064833
 
7.7%
i 3223273
 
6.1%
o 2787265
 
5.3%
t 2606633
 
4.9%
r 2080930
 
3.9%
, 1840854
 
3.5%
u 1792540
 
3.4%
m 1539332
 
2.9%
Other values (57) 17211886
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52820822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10960092
20.7%
' 4713184
 
8.9%
e 4064833
 
7.7%
i 3223273
 
6.1%
o 2787265
 
5.3%
t 2606633
 
4.9%
r 2080930
 
3.9%
, 1840854
 
3.5%
u 1792540
 
3.4%
m 1539332
 
2.9%
Other values (57) 17211886
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52820822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10960092
20.7%
' 4713184
 
8.9%
e 4064833
 
7.7%
i 3223273
 
6.1%
o 2787265
 
5.3%
t 2606633
 
4.9%
r 2080930
 
3.9%
, 1840854
 
3.5%
u 1792540
 
3.4%
m 1539332
 
2.9%
Other values (57) 17211886
32.6%
Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:28.671507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9839589
Min length6

Characters and Unicode

Total characters3601893
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13 days
2nd row234 day
3rd row616 day
4th row656 day
5th row444 day
ValueCountFrequency (%)
day 439997
42.7%
days 75741
 
7.3%
1 2585
 
0.3%
322 2308
 
0.2%
120 2284
 
0.2%
338 1963
 
0.2%
534 1940
 
0.2%
394 1904
 
0.2%
429 1860
 
0.2%
241 1803
 
0.2%
Other values (723) 499091
48.4%
2025-05-07T14:02:28.989981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
515738
14.3%
d 515738
14.3%
a 515738
14.3%
y 515738
14.3%
3 185784
 
5.2%
1 177517
 
4.9%
2 177377
 
4.9%
4 170996
 
4.7%
6 163556
 
4.5%
5 159080
 
4.4%
Other values (5) 504631
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3601893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
515738
14.3%
d 515738
14.3%
a 515738
14.3%
y 515738
14.3%
3 185784
 
5.2%
1 177517
 
4.9%
2 177377
 
4.9%
4 170996
 
4.7%
6 163556
 
4.5%
5 159080
 
4.4%
Other values (5) 504631
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3601893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
515738
14.3%
d 515738
14.3%
a 515738
14.3%
y 515738
14.3%
3 185784
 
5.2%
1 177517
 
4.9%
2 177377
 
4.9%
4 170996
 
4.7%
6 163556
 
4.5%
5 159080
 
4.4%
Other values (5) 504631
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3601893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
515738
14.3%
d 515738
14.3%
a 515738
14.3%
y 515738
14.3%
3 185784
 
5.2%
1 177517
 
4.9%
2 177377
 
4.9%
4 170996
 
4.7%
6 163556
 
4.5%
5 159080
 
4.4%
Other values (5) 504631
14.0%

lat
Real number (ℝ)

Distinct1472
Distinct (%)0.3%
Missing3268
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean49.442439
Minimum41.328376
Maximum52.400181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:29.083487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41.328376
5-th percentile41.386146
Q148.214662
median51.499981
Q351.516288
95-th percentile52.36813
Maximum52.400181
Range11.071805
Interquartile range (IQR)3.301626

Descriptive statistics

Standard deviation3.4663252
Coefficient of variation (CV)0.070108298
Kurtosis0.65444514
Mean49.442439
Median Absolute Deviation (MAD)0.0591145
Skewness-1.4036504
Sum25337767
Variance12.015411
MonotonicityNot monotonic
2025-05-07T14:02:29.169766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.5019097 4789
 
0.9%
51.5110993 4256
 
0.8%
51.5009609 4169
 
0.8%
51.499046 3578
 
0.7%
51.5108412 3212
 
0.6%
51.5109945 2958
 
0.6%
51.499981 2768
 
0.5%
51.5195688 2628
 
0.5%
51.4935083 2565
 
0.5%
51.5024348 2551
 
0.5%
Other values (1462) 478996
92.9%
(Missing) 3268
 
0.6%
ValueCountFrequency (%)
41.3283758 572
0.1%
41.368437 575
0.1%
41.3703041 229
 
< 0.1%
41.371308 1082
0.2%
41.3725246 120
 
< 0.1%
41.3727844 265
 
0.1%
41.3732462 797
0.2%
41.3747031 179
 
< 0.1%
41.3747873 158
 
< 0.1%
41.3750293 932
0.2%
ValueCountFrequency (%)
52.4001813 312
 
0.1%
52.3924898 467
 
0.1%
52.3923684 143
 
< 0.1%
52.3872884 856
0.2%
52.3856494 1071
0.2%
52.385601 1686
0.3%
52.3846059 916
0.2%
52.3840358 108
 
< 0.1%
52.3793659 845
0.2%
52.3786823 594
 
0.1%

lng
Real number (ℝ)

Distinct1472
Distinct (%)0.3%
Missing3268
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.823803
Minimum-0.3697581
Maximum16.429233
Zeros0
Zeros (%)0.0%
Negative256226
Negative (%)49.7%
Memory size3.9 MiB
2025-05-07T14:02:29.253334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3697581
5-th percentile-0.1947475
Q1-0.143372
median0.010607
Q34.834443
95-th percentile16.356445
Maximum16.429233
Range16.798991
Interquartile range (IQR)4.977815

Descriptive statistics

Standard deviation4.5794253
Coefficient of variation (CV)1.6217227
Kurtosis2.7791456
Mean2.823803
Median Absolute Deviation (MAD)0.2941333
Skewness1.8964221
Sum1447114.3
Variance20.971136
MonotonicityNot monotonic
2025-05-07T14:02:29.337899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0232208 4789
 
0.9%
-0.1208673 4256
 
0.8%
-0.1165913 4169
 
0.8%
-0.1917073 3578
 
0.7%
-0.0780581 3212
 
0.6%
-0.1863417 2958
 
0.6%
-0.1928791 2768
 
0.5%
-0.170521 2628
 
0.5%
-0.1834346 2565
 
0.5%
-0.0002497 2551
 
0.5%
Other values (1462) 478996
92.9%
(Missing) 3268
 
0.6%
ValueCountFrequency (%)
-0.3697581 413
 
0.1%
-0.3192925 391
 
0.1%
-0.306071 128
 
< 0.1%
-0.2915052 385
 
0.1%
-0.290706 680
 
0.1%
-0.2864945 1212
0.2%
-0.284704 1848
0.4%
-0.2835263 2227
0.4%
-0.282992 197
 
< 0.1%
-0.2787261 1251
0.2%
ValueCountFrequency (%)
16.4292329 41
 
< 0.1%
16.4219737 224
< 0.1%
16.4217627 426
0.1%
16.4210093 361
0.1%
16.4200957 431
0.1%
16.417026 143
 
< 0.1%
16.4133973 191
 
< 0.1%
16.4129493 501
0.1%
16.4116997 92
 
< 0.1%
16.4082294 63
 
< 0.1%

sample
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1
386803 
0
128935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters515738
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

Length

2025-05-07T14:02:29.417214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-07T14:02:29.483103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

Most occurring characters

ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 515738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 515738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 515738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 386803
75.0%
0 128935
 
25.0%

reviewer_score
Real number (ℝ)

High correlation  Zeros 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2976717
Minimum0
Maximum10
Zeros128935
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-05-07T14:02:29.551676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.625
median7.9
Q39.6
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8.975

Descriptive statistics

Standard deviation3.902295
Coefficient of variation (CV)0.61964091
Kurtosis-1.0623936
Mean6.2976717
Median Absolute Deviation (MAD)1.7
Skewness-0.78737492
Sum3247948.6
Variance15.227906
MonotonicityNot monotonic
2025-05-07T14:02:29.630378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 128935
25.0%
10 86803
16.8%
9.6 53502
10.4%
9.2 44053
 
8.5%
8.8 34795
 
6.7%
8.3 30903
 
6.0%
7.5 26164
 
5.1%
7.9 24901
 
4.8%
7.1 18529
 
3.6%
6.7 14117
 
2.7%
Other values (28) 53036
10.3%
ValueCountFrequency (%)
0 128935
25.0%
2.5 1632
 
0.3%
2.9 1211
 
0.2%
3 25
 
< 0.1%
3.1 6
 
< 0.1%
3.3 2063
 
0.4%
3.5 61
 
< 0.1%
3.8 3017
 
0.6%
4 66
 
< 0.1%
4.2 3827
 
0.7%
ValueCountFrequency (%)
10 86803
16.8%
9.6 53502
10.4%
9.5 523
 
0.1%
9.4 47
 
< 0.1%
9.2 44053
8.5%
9 483
 
0.1%
8.8 34795
6.7%
8.5 379
 
0.1%
8.3 30903
 
6.0%
8.1 28
 
< 0.1%

Interactions

2025-05-07T14:02:20.330141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:10.290704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.524097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.709652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.978484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.516328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.732216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.953355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.151690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.459080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:10.427675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.647560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.840366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:14.422896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.648625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.879270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.080385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.279568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.584572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:10.566489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.775486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.975020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:14.575768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.780647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.011560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.204253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.408819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.717373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:10.707625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.911572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.120605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:14.704006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.917106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.154819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.337733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.544765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.869876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:10.857306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.039626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.260914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:14.842760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.047923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.285897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.471337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.678521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.999508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.006058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.170872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.400626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:14.984843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.177948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.420089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.606476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.815017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:21.133618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.143198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.323846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.564179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.124414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.315218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.558190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.762375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.950078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:21.260635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.273184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.454505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.695843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.256326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.456014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.687582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:18.886258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.080259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:21.384949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:11.399028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:12.584713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:13.827863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:15.386075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:16.590932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:17.820119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:19.027460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-07T14:02:20.204378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-07T14:02:29.690291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
additional_number_of_scoringaverage_scorelatlngreview_total_negative_word_countsreview_total_positive_word_countsreviewer_scoresampletotal_number_of_reviewstotal_number_of_reviews_reviewer_has_given
additional_number_of_scoring1.000-0.1280.426-0.3850.049-0.057-0.0270.0000.859-0.105
average_score-0.1281.000-0.0860.180-0.1590.1390.2000.000-0.1940.041
lat0.426-0.0861.000-0.3240.036-0.025-0.0150.0000.151-0.101
lng-0.3850.180-0.3241.000-0.0500.0600.0350.000-0.0440.117
review_total_negative_word_counts0.049-0.1590.036-0.0501.0000.023-0.2650.0000.0520.008
review_total_positive_word_counts-0.0570.139-0.0250.0600.0231.0000.1770.000-0.0400.047
reviewer_score-0.0270.200-0.0150.035-0.2650.1771.0001.000-0.043-0.014
sample0.0000.0000.0000.0000.0000.0001.0001.0000.0000.003
total_number_of_reviews0.859-0.1940.151-0.0440.052-0.040-0.0430.0001.000-0.039
total_number_of_reviews_reviewer_has_given-0.1050.041-0.1010.1170.0080.047-0.0140.003-0.0391.000

Missing values

2025-05-07T14:02:21.568608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-07T14:02:22.084235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-07T14:02:22.786252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score
0Via Senigallia 6 20161 Milan Italy9047/21/20178.1Hotel Da VinciUnited KingdomWould have appreciated a shop in the hotel that sold drinking water etc but not necessity Would recommend if like us you arrive late at night to bring drinks from plane airport as there s no shop nearby There is a minibar though if you want to pay those prices5216670Hotel was great clean friendly staff free breakfast every morning with good selection good wifi connection nice sized room with bath fridge in room Personally loved the fact that the hotel isn t in the city centre but is literally next to a train station that you can easily get to and from the airport city Would definitely stay again621[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']13 days45.5331379.17110200.0
1Arlandaweg 10 Westpoort 1043 EW Amsterdam Netherlands61212/12/20168.6Urban Lodge HotelBelgiumNo tissue paper box was present at the room105018No Positive07[' Leisure trip ', ' Group ', ' Triple Room ', ' Stayed 1 night ']234 day52.3856494.83444300.0
2Mallorca 251 Eixample 08008 Barcelona Spain4611/26/20158.3Alexandra Barcelona A DoubleTree by HiltonSwedenPillows3351Nice welcoming and service515[' Business trip ', ' Solo traveler ', ' Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']616 day41.3931922.16152000.0
3Piazza Della Repubblica 17 Central Station 20124 Milan Italy24110/17/20159.1Hotel Principe Di SavoiaUnited States of AmericaNo Negative01543Everything including the nice upgrade The Hotel has been revamped and what a surprise Love every second of it including in room dining which was excellent279[' Leisure trip ', ' Couple ', ' Ambassador Junior Suite ', ' Stayed 1 night ']656 day45.4798889.19629800.0
4Singel 303 309 Amsterdam City Center 1012 WJ Amsterdam Netherlands8345/16/20169.1Hotel Esther aUnited KingdomNo Negative04687Lovely hotel v welcoming staff72[' Business trip ', ' Solo traveler ', ' Classic Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']444 day52.3705454.88864400.0
5Coram Street Camden London WC1N 1HT United Kingdom7098/13/20158.2Holiday Inn London BloomsburyEcuadorThey don t have free wifi72995The location is perfect if you don t have a lot of time and you want to have a look at the city centre263[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 1 night ']721 day51.524125-0.12580700.0
6Empire Way Wembley Brent London HA9 8DS United Kingdom10058/18/20168.3Holiday Inn London WembleyUnited KingdomRoom generally a bit shabby with some lack of maintenance Some crumbs on bedroom floor these issues did not spoil our minibreak It would be nice to have vegetarian sausages available for breakfast353469Location price It did not cost much more to have breakfast included Room was a reasonable size and bed was comfortable2311[' Leisure trip ', ' Couple ', ' Queen Room ', ' Stayed 1 night ']350 day51.559095-0.28470400.0
71 Shortlands Hammersmith and Fulham London W6 8DR United Kingdom7048/11/20158.3Novotel London WestNetherlandsExecutive rooms 9th Floor don t have a bath Their website made it look like all rooms did have one and when being at the end of a hall there s no wifi connection possible Mind that during my first two stays here I did have a perfect wifi connection522443Comphy bed upgraded to executive room with nespresso machine etc for only 24 3 nights quiet room clean 4 free waters in the fridge tho no refill and close to Hammersmith station shops and Starbucks Olympia is in walking distance too4238[' Business trip ', ' Solo traveler ', ' Executive Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']723 day51.491959-0.22009600.0
835 Rue Caumartin 9th arr 75009 Paris France2116/25/20168.9Hotel Saint Petersbourg OperaIrelandPity about the two days of rain82412Its centrality proximity to our destination71[' Group ', ' Double or Twin Room ', ' Stayed 1 night ']404 day48.8721742.32807500.0
949 Gloucester Place Marble Arch Westminster Borough London W1U 8JE United Kingdom619/30/20157.4St George HotelCanadaDidn t like it at all construction was in progress stuff lied to us about vacancy18334Didn t like anything about the stay if i had a chance to change or cancel it I would do it right away251[' Couple ', ' Standard Triple Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']673 day51.518277-0.15835100.0
hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score
5157283 rue de Ponthieu 8th arr 75008 Paris France7012/1/20168.7H tel Mathis Elys esUnited States of AmericaNo Negative0652Location25[' Leisure trip ', ' Group ', ' Junior Suite ', ' Stayed 2 nights ', ' Submitted from a mobile device ']245 day48.8700332.31127419.6
51572915 Rue Boissy d Anglas 8th arr 75008 Paris France9112/21/20168.5Sofitel Paris Le FaubourgUnited Arab EmiratesNo Negative0564Location was perfect Room was very comfortable spacious1040[' Leisure trip ', ' Solo traveler ', ' Luxury Room 1 Queensize Bed Twin bedded Room On Request ', ' Stayed 6 nights ', ' Submitted from a mobile device ']225 day48.8684142.32132519.2
51573052 56 Inverness Terrace Westminster Borough London W2 3LB United Kingdom54510/12/20158.0Shaftesbury Hyde Park InternationalUnited Kingdomstaff miserable and room very small72907Outstanding location31[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ']661 day51.512397-0.18612415.8
51573122 Portman Square Westminster Borough London W1H 7BG United Kingdom5978/16/20167.9Radisson Blu Portman Hotel LondonUnited KingdomRoom was very small so much so that I kept hitting myself on the TV that was mounted on the wall Bed was very soft and pillows were awful302308Staff were friendly and efficient63[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']352 day51.516191-0.15794915.0
51573224 Ludgate Hill City of London London EC4M 7DR United Kingdom91811/8/20158.4Club Quarters Hotel St Paul sSwedenOur room was really cold and we had problem with the heater They had to bring a portable heater to fix the issue254117It is a nice and clean hotel with a good location133[' Leisure trip ', ' Group ', ' Standard Queen Room ', ' Stayed 3 nights ']634 day51.513930-0.10112617.9
5157339 Knaresborough Place Kensington and Chelsea London SW5 0TP United Kingdom1074/19/20179.0Hotel MoonlightFranceNo Negative0617Tr s proche du metro Earl s court1010[' Leisure trip ', ' Group ', ' Club Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']106 day51.494028-0.19105018.8
515734Landstra er Hauptstra e 155 03 Landstra e 1030 Vienna Austria2722/13/20178.4BEST WESTERN PLUS Amedia WienTurkeyNo Negative03224The bed was so comfy I stayed with my boyfriend we had a double bed Also transportation is excellent the hotel is very very close to Old City Once you exit the hotel just turn right about 50m away there is a bus stop get off on Stubentor it is the last stop It only takes 10min Also you can take the same bus back to the hotel The bus name is 74A St Marx The hotel was very clean and the room that we accomidated in was nice and roomy931[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 4 nights ', ' Submitted from a mobile device ']171 day48.19237916.39945119.2
51573529 31 Gower Street Camden London WC1E 6HG United Kingdom4572/7/20166.8Bloomsbury Palace HotelNetherlandsroom is really small but guess is normal in London122751great location simple check in out nice shower921[' Business trip ', ' Solo traveler ', ' Single Room ', ' Stayed 1 night ']543 day51.520795-0.13108418.3
51573631 Great Cumberland Place Westminster Borough London W1H 7TA United Kingdom3655/21/20178.1The Marble Arch LondonUnited Arab EmiratesNo Negative01567Location and very comfy bed628[' Leisure trip ', ' Solo traveler ', ' Deluxe Double Room ', ' Stayed 2 nights ']74 days51.515125-0.16006619.2
51573725 Courtfield Gardens Kensington and Chelsea London SW5 0PG United Kingdom2228/5/20169.0The Nadler KensingtonAustraliaPatio outside could have been cleaned of algae to give a more uplifting atmosphere to a downstairs room201209Beds comfortable Pillows also good Homely feel although room was small Staff very pleasant and helpful thank you202[' Leisure trip ', ' Couple ', ' Bunk Bed Room ', ' Stayed 4 nights ']363 day51.493109-0.19020818.8

Duplicate rows

Most frequently occurring

hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score# duplicates
016 22 Great Russell Street Camden London WC1B 3NN United Kingdom3007/27/20179.0The Bloomsbury HotelIsraelNo Negative01254The attention received by Sebastian and his team was exceptional124[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']7 days51.517167-0.12905319.62
1167 rue de Rome 17th arr 75017 Paris France1110/14/20166.8Villa EugenieIranEvry thing was wrong Cold room Dark room No refrigetor in room No ac Evry thing was baaaaaaad Very bad21165This hotel was terrible this place worst place in paris Hostel is better than this place I m sorry for myself to spend my time in this place292[' Solo traveler ', ' Single Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']293 day48.8871282.31420512.52
2167 rue de Rome 17th arr 75017 Paris France1110/14/20166.8Villa EugenieUnited KingdomEvry thing of this place i can t name horlrel to this place was wrong and out of repair all lamp were nt work 2nights i got cold in cold room Ac not working i so sorry for my self that i had to spend my time in this ruin place 4star hotel has nt refrigerator58165This hotel is worst hotel Its terrible91[' Solo traveler ', ' Single Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']293 day48.8871282.31420512.52
3167 rue de Rome 17th arr 75017 Paris France1110/18/20166.8Villa EugenieIsraelNo Negative0165the room was very french and beautiful it was good location and I enjoyed to stay there181[' Leisure trip ', ' Couple ', ' Twin Room ', ' Stayed 3 nights ']289 day48.8871282.31420519.22
4167 rue de Rome 17th arr 75017 Paris France1110/2/20166.8Villa EugenieQatarStaff very rude My credit card was charged Before my stay and while Checking out they charged me again when I told the receptionist about it her answer was I m not trying to steal your money madam in a ver un polite way Blaming my Bank about it Very poor selection for breakfast56165Nothing38[' Leisure trip ', ' Family with young children ', ' Two Connecting Double Rooms ', ' Stayed 2 nights ', ' Submitted from a mobile device ']305 day48.8871282.31420515.02
5167 rue de Rome 17th arr 75017 Paris France1112/12/20166.8Villa EugenieCanadaListed above3165It was a terrible stat unfriendly staff very unprofessional and dirty rooms131[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 6 nights ', ' Submitted from a mobile device ']234 day48.8871282.31420500.02
6167 rue de Rome 17th arr 75017 Paris France115/26/20166.8Villa EugenieFrancevery dated d cor certainly NOT a 4 star hotel11165bed was confy central friendliy staff71[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 2 nights ']434 day48.8871282.31420516.72
7167 rue de Rome 17th arr 75017 Paris France118/2/20176.8Villa EugenieUnited States of AmericaPlace is old not worthy of 4 stars9165Location friendly staff cell phone for use during stay in Paris1221[' Leisure trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']1 days48.8871282.31420517.12
8167 rue de Rome 17th arr 75017 Paris France119/19/20156.8Villa EugenieSwitzerlandFacilities did not function Sink was blocked Something heavy fell If I had been in the room I would have been injured Way overpriced for what it is30165The staff tried54[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 1 night ']684 day48.8871282.31420514.22
9167 rue de Rome 17th arr 75017 Paris France119/22/20166.8Villa EugenieItalyBed bugs air condition not work7165Front office is helpfull51[' Leisure trip ', ' Family with young children ', ' Standard Double or Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']315 day48.8871282.31420515.02